Counting, sizing, and identification of algae using image analysis

A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.

A Laplacian characterization of phytoplankton shape

Phytoplankton exhibit pronounced morphological diversity, impacting a range of processes. Because these impacts are challenging to quantify, however, phytoplankton are often approximated as spheres, and when effects of non-sphericity are studied it is usually experimentally or via geometrical approximations. New methods for quantifying phytoplankton size and shape generally, so all phytoplankton are analyzable by the same procedure, can complement advances in microscopic imagery and automated classification to study the influence of shape in phytoplankton. Here we apply to phytoplankton a technique for defining the size of arbitrary shapes based on the Laplacian-the operator that governs processes, such as nutrient uptake and fluid flow, where phytoplankton shape is expected to have the greatest effect. Deviations from values given by spherical approximation are a measure of phytoplankton shape and indicate the fitness increases for phytoplankton conferred by their non-spherical shapes. Comparison with surface-to-volume quotients suggests the Laplacian-based metric is insensitive to small-scale features which can increase surface area without affecting key processes, but is otherwise closely related to surface-area-to-volume, demonstrating this metric is a meaningful measure. While our analysis herein is limited to axisymmetric phytoplankton due to relative sparsity of 3D information about other phytoplankton shapes, the definition and method are directly generalizable to 3D shape data, which will in the near future be more readily available.